Pulmonary embolism (PE) is a relatively common cardiovascular emergency with about 600,000 cases occurring annually and causing approximately 200,000 deaths in the United States per year. A pulmonary embolus usually starts from the lower extremity, travels in the bloodstream through the heart and into the lungs, gets lodged in the pulmonary arteries, and subsequently blocks blood flow into, and oxygen exchange in, the lungs, leading to sudden death. Based on its relative location in the pulmonary arteries, an embolus may be classified into four groups (central, lobar, segmental and sub-segmental).
Computed tomography pulmonary angiography (CTPA) has become the test of choice for PE diagnosis. The interpretation of CTPA image datasets is made complex and time consuming by the intricate branching structure of the pulmonary vessels, a myriad of artifacts that may obscure or mimic PEs, and suboptimal bolus of contrast and inhomogeneity with the pulmonary arterial blood pool.
Several approaches for computer-aided diagnosis of PE in CTPA have been proposed. However, these approaches are not adequately capable of detecting central PEs, distinguishing the pulmonary artery from the vein to effectively remove any false positives from the veins, and dynamically adapting to suboptimal contrast conditions associated the CTPA scans.
Accordingly, new mechanisms for detecting an anatomical object in a medical device image are needed.